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Conference Papers Year : 2017

Boosting Cross-Age Face Verification via Generative Age Normalization

Grigory Antipov
Jean-Luc Dugelay
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Abstract

Despite the tremendous progress in face verification performance as a result of Deep Learning, the sensitivity to human age variations remains an Achilles' heel of the majority of the contemporary face verification software. A promising solution to this problem consists in synthetic aging/rejuvenation of the input face images to some predefined age categories prior to face verification. We recently proposed Age-cGAN aging/rejuvenation method based on generative adversarial neural networks allowing to synthesize more plausible and realistic faces than alternative non-generative methods. However, in this work, we show that Age-cGAN cannot be directly used for improving face verification due to its slightly imperfect preservation of the original identities in aged/rejuvenated faces. We therefore propose Local Manifold Adaptation (LMA) approach which resolves the stated issue of Age-cGAN resulting in the novel Age-cGAN+LMA aging/rejuvenation method. Based on Age-cGAN+LMA, we design an age normalization algorithm which boosts the accuracy of an off-the-shelf face verification software in the cross-age evaluation scenario.
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Dates and versions

hal-01617381 , version 1 (16-10-2017)

Identifiers

  • HAL Id : hal-01617381 , version 1

Cite

Grigory Antipov, Moez Baccouche, Jean-Luc Dugelay. Boosting Cross-Age Face Verification via Generative Age Normalization. International Joint Conference on Biometrics, Oct 2017, Denver, United States. ⟨hal-01617381⟩

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